Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Weizman Orianne, Duceau Baptiste, Trimaille Antonin, Pommier Thibaut, Cellier Joffrey, Geneste Laura, Panagides Vassili, Marsou Wassima, Deney Antoine, Attou Sabir, Delmotte Thomas, Ribeyrolles Sophie, Chemaly Pascale, Karsenty Clément, Giordano Gauthier, Gautier Alexandre, Chaumont Corentin, Guilleminot Pierre, Sagnard Audrey, Pastier Julie, Ezzouhairi Nacim, Perin Benjamin, Zakine Cyril, Levasseur Thomas, Ma Iris, Chavignier Diane, Noirclerc Nathalie, Darmon Arthur, Mevelec Marine, Sutter Willy, Mika Delphine, Fauvel Charles, Pezel Théo, Waldmann Victor, Cohen Ariel, Bonnet Guillaume, Critical COVID-19 France investigators
Аннотация
AIMS: To develop and validate a score to predict outcomes in patients hospitalized with COVID-19.
METHODS: All consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort.
RESULTS: Among 2873 patients analysed (57.9% men; 66.6±17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n=2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75-0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores.
CONCLUSIONS: The CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.